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by claude@2026-07, 2026-07-03
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The study develops a technical extension to source-based morphometry by transforming each subject’s SBM component loading vector into a symmetric “co-modulation” matrix via an outer product, where each matrix element captures individualized pairwise interaction between structural components. Using structural MRI from 210 schizophrenia patients and 195 healthy controls from the fBIRN dataset, with functional networks used as priors for SBM, the authors found widespread reductions in structural co-modulation in schizophrenia, especially within and between visual, default-mode, and cognitive control networks. They report that co-modulation patterns were significantly associated with cognitive performance and clinical symptom severity among patients. The main limitation stated is methodological: co-modulation is derived from SBM loadings and relies on the use of functional networks as priors. The paper does not explicitly discuss endometriosis or adenomyosis; it was included in the corpus via a keyword match in the upstream search index.
Abstract
Source-based morphometry (SBM) is a powerful multivariate method for identifying covarying structural brain networks. However, standard SBM provides only a single loading value per component for each subject, which limits the characterization of relationships between these components. We propose a novel technical co-modulation approach to derive an individualized, network-like measure of structural brain organization. This method transforms the subject-specific SBM loading vector into a symmetric co-modulation matrix by computing the vector’s outer product. Each element of this matrix quantifies the pairwise interaction between structural components, creating a subject-specific fingerprint. Similar to functional connectivity that maps the temporal synchronization between networks, this matrix maps their joint structural prominence, reflecting how strongly two networks co-occur within an individual. To demonstrate the utility of this method, we applied it to structural MRI data from 210 patients with schizophrenia (SZ) and 195 healthy controls (HC) from the fBIRN psychosis dataset using functional networks as priors for SBM. We observed widespread reductions in structural co-modulation in the SZ group, particularly within and between visual, default-mode, and cognitive control networks. Furthermore, co-modulation patterns were significantly correlated with cognitive performance and clinical symptom severity in patients. Structural co-modulation provides a robust framework for quantifying individualized relationships between structural brain features, overcoming key limitations of standard SBM and offering a new avenue for integrating structural and functional brain analyses.
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Abstract
Source-based morphometry (SBM) is a powerful multivariate method for identifying covarying structural brain networks. However, standard SBM provides only a single loading value per component for each subject, which limits the characterization of relationships between these components. We propose a novel technical co-modulation approach to derive an individualized, network-like measure of structural brain organization. This method transforms the subject-specific SBM loading vector into a symmetric co-modulation matrix by computing the vector’s outer product. Each element of this matrix quantifies the pairwise interaction between structural components, creating a subject-specific fingerprint. Similar to functional connectivity that maps the temporal synchronization between networks, this matrix maps their joint structural prominence, reflecting how strongly two networks co-occur within an individual. To demonstrate the utility of this method, we applied it to structural MRI data from 210 patients with schizophrenia (SZ) and 195 healthy controls (HC) from the fBIRN psychosis dataset using functional networks as priors for SBM. We observed widespread reductions in structural co-modulation in the SZ group, particularly within and between visual, default-mode, and cognitive control networks. Furthermore, co-modulation patterns were significantly correlated with cognitive performance and clinical symptom severity in patients. Structural co-modulation provides a robust framework for quantifying individualized relationships between structural brain features, overcoming key limitations of standard SBM and offering a new avenue for integrating structural and functional brain analyses.
Competing Interest Statement
The authors have declared no competing interest.
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